Abstract:Measurements in biological and biomedical systems are often noisy, with statistical properties that can change over a range of time scales. These properties stem from the stochastic, nonlinear, non-stationary, and distributed nature of many biological/biomedical systems, whose internal states we wish to observe or estimate. Consequently, statistical signal processing tools, suitable for analysis of signals in man-made systems, may be inadequate in addressing challenges specific to biosystems.

Advances in sensor technology and computer architecture have allowed ever increasing volumes of biomedical data to be acquired and stored. Processing of this data presents several challenges such as: automated analysis; unsupervised (blind) processing; multi-sensor fusion; and, statistical sparseness. A systematic approach to these challenges very often requires an entirely new set of tools to be developed and implemented. In this lecture, we will present an overview of some signal processing and data analysis challenges in several biomedical disciplines. We will also present some original solutions to these problems and discuss potential applications to other (non-biomedical) disciplines.

About the Speaker:Zoran Nenadic received a Diploma degree in control engineering from the University of Belgrade, Serbia, in 1995, and M.S. and D.Sc. degrees in systems science and mathematics from Washington University, St. Louis, Mo., in 1998 and 2001, respectively. From 2001 to 2005, he was a postdoctoral scholar with the Division of Engineering and Applied Science at the California Institute of Technology, Pasadena. Since 2005, he has been with the Department of Biomedical Engineering in The Henry Samueli School of Engineering at the University of California, Irvine, where he is currently an assistant professor. His research interests are in the area of adaptive biomedical signal processing, control algorithms for biomedical devices, brain–machine interfaces, and modeling and analysis of biological neural networks. Nenadic is a member of the Institute of Electrical and Electronics Engineers, the Mathematical Association of America, and the Society for Neuroscience.